The fastest method for installing this model locally is by using Docker.
Refer to the action plan below to initialize the model.
The installer automatically pulls the model (could be multiple GBs).
The installer will automatically analyze your hardware and select the optimal configuration.
SmolLM3-3B is a compact language model designed for efficient inference on consumer hardware. It leverages a refined architecture that balances parameter count and context length, delivering strong performance in both reasoning and generation tasks. The model supports up to 8K tokens of context, enabling it to handle longer dialogues and documents without truncation. Benchmarks show it outperforms similarly sized models in multilingual understanding and code generation. Its training pipeline incorporates extensive data filtering and instruction tuning, resulting in coherent and factual outputs. The compact footprint makes it ideal for deployment in edge devices and research prototypes.
| Parameter | Value |
|---|---|
| Parameters | 3 B |
| Context Length | 8K tokens |
| Training Data | ≈1.5 TB filtered corpus |
| Inference Speed | ~120 tokens/s on GPU |
- Installer configuring distributed tensor calculation grids across multiple local desktop systems
- Quick Run SmolLM3-3B Locally (No Cloud) Easy Build
- Downloader for specialized AnimateDiff v3 motion modules for local video
- SmolLM3-3B via WebGPU (Browser) Full Speed NPU Mode FREE
- Installer configuring custom chat templates for local inference
- Zero-Click Run SmolLM3-3B on Your PC No-Internet Version
- Script downloading specialized multi-column layout parsing models for PDF engine scrapers
- SmolLM3-3B on Your PC Full Speed NPU Mode